Abstract
Document retrieval aims at finding the most important documents where a pattern appears in a collection of strings. Traditional pattern-matching techniques yield brute-force document retrieval solutions, which has motivated the research on tailored indexes that offer near-optimal performance. However, an experimental study establishing which alternatives are actually better than brute force, and which perform best depending on the collection characteristics, has not been carried out. In this paper we address this shortcoming by exploring the relationship between the nature of the underlying collection and the performance of current methods. Via extensive experiments we show that established solutions are often beaten in practice by brute-force alternatives. We also design new methods that offer superior time/space trade-offs, particularly on repetitive collections.
This work is funded in part by: Fondecyt Project 1-140796 (first author); Basal Funds FB0001, Conicyt, Chile (first and third authors); the Jenny and Antti Wihuri Foundation, Finland (third author); and by the Academy of Finland through grants 258308 and 250345 (CoECGR) (second author).
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Navarro, G., Puglisi, S.J., Sirén, J. (2014). Document Retrieval on Repetitive Collections. In: Schulz, A.S., Wagner, D. (eds) Algorithms - ESA 2014. ESA 2014. Lecture Notes in Computer Science, vol 8737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44777-2_60
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DOI: https://doi.org/10.1007/978-3-662-44777-2_60
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